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Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects

We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved...

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Detalles Bibliográficos
Autores principales: Manyakov, Nikolay V., Chumerin, Nikolay, Combaz, Adrien, Van Hulle, Marc M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3175727/
https://www.ncbi.nlm.nih.gov/pubmed/21941530
http://dx.doi.org/10.1155/2011/519868
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author Manyakov, Nikolay V.
Chumerin, Nikolay
Combaz, Adrien
Van Hulle, Marc M.
author_facet Manyakov, Nikolay V.
Chumerin, Nikolay
Combaz, Adrien
Van Hulle, Marc M.
author_sort Manyakov, Nikolay V.
collection PubMed
description We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.
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spelling pubmed-31757272011-09-22 Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects Manyakov, Nikolay V. Chumerin, Nikolay Combaz, Adrien Van Hulle, Marc M. Comput Intell Neurosci Research Article We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects. Hindawi Publishing Corporation 2011 2011-09-18 /pmc/articles/PMC3175727/ /pubmed/21941530 http://dx.doi.org/10.1155/2011/519868 Text en Copyright © 2011 Nikolay V. Manyakov et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Manyakov, Nikolay V.
Chumerin, Nikolay
Combaz, Adrien
Van Hulle, Marc M.
Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects
title Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects
title_full Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects
title_fullStr Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects
title_full_unstemmed Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects
title_short Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects
title_sort comparison of classification methods for p300 brain-computer interface on disabled subjects
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3175727/
https://www.ncbi.nlm.nih.gov/pubmed/21941530
http://dx.doi.org/10.1155/2011/519868
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